Introduction to machine learning [4th ed.] (Record no. 567345)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02369 a2200217 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262043793 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | IIT Kanpur |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | Al74i4 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Alpaydın, Ethem |
| 245 ## - TITLE STATEMENT | |
| Title | Introduction to machine learning [4th ed.] |
| Statement of responsibility, etc | Ethem Alpaydın |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 4th ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | MIT Press |
| Year of publication | 2020 |
| Place of publication | Cambridge |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xxiv, 682p |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | Adaptive computation and machine learning |
| 490 ## - SERIES STATEMENT | |
| Series statement | / edited by Francis Bach |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.<br/><br/>The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.<br/><br/>The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| General Stacks | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 09/12/2024 | 2 | 5360.10 | 006.31 Al74i4 | A186627 | 7146.80 | Books |